{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ch `12`: Concept `02`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image embedding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The VGG-16 TensorFlow port is by Davi Frossard (http://www.cs.toronto.edu/~frossard/post/vgg16/). \n",
    "\n",
    "Along with TensorFlow, it requires the following libraries:\n",
    "\n",
    "```bash\n",
    "$ pip install scipy\n",
    "$ pip install Pillow\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will need to download the model parameters\n",
    "\n",
    "```bash\n",
    "$ wget https://www.cs.toronto.edu/~frossard/vgg16/vgg16_weights.npz\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model...\n",
      "0 conv1_1_W (3, 3, 3, 64)\n",
      "1 conv1_1_b (64,)\n",
      "2 conv1_2_W (3, 3, 64, 64)\n",
      "3 conv1_2_b (64,)\n",
      "4 conv2_1_W (3, 3, 64, 128)\n",
      "5 conv2_1_b (128,)\n",
      "6 conv2_2_W (3, 3, 128, 128)\n",
      "7 conv2_2_b (128,)\n",
      "8 conv3_1_W (3, 3, 128, 256)\n",
      "9 conv3_1_b (256,)\n",
      "10 conv3_2_W (3, 3, 256, 256)\n",
      "11 conv3_2_b (256,)\n",
      "12 conv3_3_W (3, 3, 256, 256)\n",
      "13 conv3_3_b (256,)\n",
      "14 conv4_1_W (3, 3, 256, 512)\n",
      "15 conv4_1_b (512,)\n",
      "16 conv4_2_W (3, 3, 512, 512)\n",
      "17 conv4_2_b (512,)\n",
      "18 conv4_3_W (3, 3, 512, 512)\n",
      "19 conv4_3_b (512,)\n",
      "20 conv5_1_W (3, 3, 512, 512)\n",
      "21 conv5_1_b (512,)\n",
      "22 conv5_2_W (3, 3, 512, 512)\n",
      "23 conv5_2_b (512,)\n",
      "24 conv5_3_W (3, 3, 512, 512)\n",
      "25 conv5_3_b (512,)\n",
      "26 fc6_W (25088, 4096)\n",
      "27 fc6_b (4096,)\n",
      "28 fc7_W (4096, 4096)\n",
      "29 fc7_b (4096,)\n",
      "30 fc8_W (4096, 1000)\n",
      "31 fc8_b (1000,)\n",
      "Done loading!\n"
     ]
    },
    {
     "data": {
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uvU/doR7x0Km/T1w3uI9I8Ra44R3jeddTln3M2rjV9pnPhem9Nnzwfm0MS3ia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UQTSxePp7Vm3aYSdOKteHrHACW72sSJMLJOyDiOPlC9pdCRnRmTgiiGuka04jpQu26LwXpxa63W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\n11SV37DGGA4Pj4ii0Ht5vA+kXq1WPHjwgKI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4AkITmIaJ7/sqgWtZJwwjdlOY6WUdz/Wp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18ac2e5828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FxNT8uLq2UNIY4BvATGAKMFfSlHYaa2Zm7Rsy+CPiFmB1C8ueBiyNiIcj4o/Ad4HZLSzH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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18ec91f7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 predictions of VGG-16 model:\n",
      "1. weasel (0.6933859586715698)\n",
      "2. polecat, fitch, foulmart, foumart, Mustela putorius (0.1753876656293869)\n",
      "3. mink (0.12208586186170578)\n",
      "4. black-footed ferret, ferret, Mustela nigripes (0.008870664052665234)\n",
      "5. otter (0.00012108328519389033)\n"
     ]
    }
   ],
   "source": [
    "########################################################################################\n",
    "# Davi Frossard, 2016                                                                  #\n",
    "# VGG16 implementation in TensorFlow                                                   #\n",
    "# Details:                                                                             #\n",
    "# http://www.cs.toronto.edu/~frossard/post/vgg16/                                      #\n",
    "#                                                                                      #\n",
    "# Model from https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md     #\n",
    "# Weights from Caffe converted using https://github.com/ethereon/caffe-tensorflow      #\n",
    "########################################################################################\n",
    "\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from scipy.misc import imread, imresize\n",
    "from imagenet_classes import class_names\n",
    "\n",
    "\n",
    "class vgg16:\n",
    "    def __init__(self, imgs, weights=None, sess=None):\n",
    "        self.imgs = imgs\n",
    "        tf.summary.image(\"imgs\", self.imgs)\n",
    "        self.convlayers()\n",
    "        self.fc_layers()\n",
    "        tf.summary.histogram(\"fc2\", self.fc2)\n",
    "        self.probs = tf.nn.softmax(self.fc3l)\n",
    "        if weights is not None and sess is not None:\n",
    "            self.load_weights(weights, sess)\n",
    "\n",
    "\n",
    "    def convlayers(self):\n",
    "        self.parameters = []\n",
    "\n",
    "        # zero-mean input\n",
    "        with tf.name_scope('preprocess') as scope:\n",
    "            mean = tf.constant([123.68, 116.779, 103.939], dtype=tf.float32, shape=[1, 1, 1, 3], name='img_mean')\n",
    "            images = self.imgs-mean\n",
    "\n",
    "        # conv1_1\n",
    "        with tf.name_scope('conv1_1') as scope:\n",
    "            kernel = tf.Variable(tf.truncated_normal([3, 3, 3, 64], dtype=tf.float32,\n",
    "                                                     stddev=1e-1), name='weights')\n",
    "            conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')\n",
    "            biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),\n",
    "                                 trainable=True, name='biases')\n",
    "            out = tf.nn.bias_add(conv, biases)\n",
    "            self.conv1_1 = tf.nn.relu(out, name=scope)\n",
    "            self.parameters += [kernel, biases]\n",
    "\n",
    "        # conv1_2\n",
    "        with tf.name_scope('conv1_2') as scope:\n",
    "            kernel = tf.Variable(tf.truncated_normal([3, 3, 64, 64], dtype=tf.float32,\n",
    "                                                     stddev=1e-1), name='weights')\n",
    "            conv = tf.nn.conv2d(self.conv1_1, kernel, [1, 1, 1, 1], padding='SAME')\n",
    "            biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),\n",
    "                                 trainable=True, name='biases')\n",
    "            out = tf.nn.bias_add(conv, biases)\n",
    "            self.conv1_2 = tf.nn.relu(out, name=scope)\n",
    "            self.parameters += [kernel, biases]\n",
    "\n",
    "        # pool1\n",
    "        self.pool1 = tf.nn.max_pool(self.conv1_2,\n",
    "                               ksize=[1, 2, 2, 1],\n",
    "                               strides=[1, 2, 2, 1],\n",
    "                               padding='SAME',\n",
    "                               name='pool1')\n",
    "\n",
    "        # conv2_1\n",
    "        with tf.name_scope('conv2_1') as scope:\n",
    "            kernel = tf.Variable(tf.truncated_normal([3, 3, 64, 128], dtype=tf.float32,\n",
    "                                                     stddev=1e-1), name='weights')\n",
    "            conv = tf.nn.conv2d(self.pool1, kernel, [1, 1, 1, 1], padding='SAME')\n",
    "            biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32),\n",
    "                                 trainable=True, name='biases')\n",
    "            out = tf.nn.bias_add(conv, biases)\n",
    "            self.conv2_1 = tf.nn.relu(out, name=scope)\n",
    "            self.parameters += [kernel, biases]\n",
    "\n",
    "        # conv2_2\n",
    "        with tf.name_scope('conv2_2') as scope:\n",
    "            kernel = tf.Variable(tf.truncated_normal([3, 3, 128, 128], dtype=tf.float32,\n",
    "                                                     stddev=1e-1), name='weights')\n",
    "            conv = tf.nn.conv2d(self.conv2_1, kernel, [1, 1, 1, 1], padding='SAME')\n",
    "            biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32),\n",
    "                                 trainable=True, name='biases')\n",
    "            out = tf.nn.bias_add(conv, biases)\n",
    "            self.conv2_2 = tf.nn.relu(out, name=scope)\n",
    "            self.parameters += [kernel, biases]\n",
    "\n",
    "        # pool2\n",
    "        self.pool2 = tf.nn.max_pool(self.conv2_2,\n",
    "                               ksize=[1, 2, 2, 1],\n",
    "                               strides=[1, 2, 2, 1],\n",
    "                               padding='SAME',\n",
    "                               name='pool2')\n",
    "\n",
    "        # conv3_1\n",
    "        with tf.name_scope('conv3_1') as scope:\n",
    "            kernel = tf.Variable(tf.truncated_normal([3, 3, 128, 256], dtype=tf.float32,\n",
    "                                                     stddev=1e-1), name='weights')\n",
    "            conv = tf.nn.conv2d(self.pool2, kernel, [1, 1, 1, 1], padding='SAME')\n",
    "            biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),\n",
    "                                 trainable=True, name='biases')\n",
    "            out = tf.nn.bias_add(conv, biases)\n",
    "            self.conv3_1 = tf.nn.relu(out, name=scope)\n",
    "            self.parameters += [kernel, biases]\n",
    "\n",
    "        # conv3_2\n",
    "        with tf.name_scope('conv3_2') as scope:\n",
    "            kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256], dtype=tf.float32,\n",
    "                                                     stddev=1e-1), name='weights')\n",
    "            conv = tf.nn.conv2d(self.conv3_1, kernel, [1, 1, 1, 1], padding='SAME')\n",
    "            biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),\n",
    "                                 trainable=True, name='biases')\n",
    "            out = tf.nn.bias_add(conv, biases)\n",
    "            self.conv3_2 = tf.nn.relu(out, name=scope)\n",
    "            self.parameters += [kernel, biases]\n",
    "\n",
    "        # conv3_3\n",
    "        with tf.name_scope('conv3_3') as scope:\n",
    "            kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256], dtype=tf.float32,\n",
    "                                                     stddev=1e-1), name='weights')\n",
    "            conv = tf.nn.conv2d(self.conv3_2, kernel, [1, 1, 1, 1], padding='SAME')\n",
    "            biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),\n",
    "                                 trainable=True, name='biases')\n",
    "            out = tf.nn.bias_add(conv, biases)\n",
    "            self.conv3_3 = tf.nn.relu(out, name=scope)\n",
    "            self.parameters += [kernel, biases]\n",
    "\n",
    "        # pool3\n",
    "        self.pool3 = tf.nn.max_pool(self.conv3_3,\n",
    "                               ksize=[1, 2, 2, 1],\n",
    "                               strides=[1, 2, 2, 1],\n",
    "                               padding='SAME',\n",
    "                               name='pool3')\n",
    "\n",
    "        # conv4_1\n",
    "        with tf.name_scope('conv4_1') as scope:\n",
    "            kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 512], dtype=tf.float32,\n",
    "                                                     stddev=1e-1), name='weights')\n",
    "            conv = tf.nn.conv2d(self.pool3, kernel, [1, 1, 1, 1], padding='SAME')\n",
    "            biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),\n",
    "                                 trainable=True, name='biases')\n",
    "            out = tf.nn.bias_add(conv, biases)\n",
    "            self.conv4_1 = tf.nn.relu(out, name=scope)\n",
    "            self.parameters += [kernel, biases]\n",
    "\n",
    "        # conv4_2\n",
    "        with tf.name_scope('conv4_2') as scope:\n",
    "            kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,\n",
    "                                                     stddev=1e-1), name='weights')\n",
    "            conv = tf.nn.conv2d(self.conv4_1, kernel, [1, 1, 1, 1], padding='SAME')\n",
    "            biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),\n",
    "                                 trainable=True, name='biases')\n",
    "            out = tf.nn.bias_add(conv, biases)\n",
    "            self.conv4_2 = tf.nn.relu(out, name=scope)\n",
    "            self.parameters += [kernel, biases]\n",
    "\n",
    "        # conv4_3\n",
    "        with tf.name_scope('conv4_3') as scope:\n",
    "            kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,\n",
    "                                                     stddev=1e-1), name='weights')\n",
    "            conv = tf.nn.conv2d(self.conv4_2, kernel, [1, 1, 1, 1], padding='SAME')\n",
    "            biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),\n",
    "                                 trainable=True, name='biases')\n",
    "            out = tf.nn.bias_add(conv, biases)\n",
    "            self.conv4_3 = tf.nn.relu(out, name=scope)\n",
    "            self.parameters += [kernel, biases]\n",
    "\n",
    "        # pool4\n",
    "        self.pool4 = tf.nn.max_pool(self.conv4_3,\n",
    "                               ksize=[1, 2, 2, 1],\n",
    "                               strides=[1, 2, 2, 1],\n",
    "                               padding='SAME',\n",
    "                               name='pool4')\n",
    "\n",
    "        # conv5_1\n",
    "        with tf.name_scope('conv5_1') as scope:\n",
    "            kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,\n",
    "                                                     stddev=1e-1), name='weights')\n",
    "            conv = tf.nn.conv2d(self.pool4, kernel, [1, 1, 1, 1], padding='SAME')\n",
    "            biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),\n",
    "                                 trainable=True, name='biases')\n",
    "            out = tf.nn.bias_add(conv, biases)\n",
    "            self.conv5_1 = tf.nn.relu(out, name=scope)\n",
    "            self.parameters += [kernel, biases]\n",
    "\n",
    "        # conv5_2\n",
    "        with tf.name_scope('conv5_2') as scope:\n",
    "            kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,\n",
    "                                                     stddev=1e-1), name='weights')\n",
    "            conv = tf.nn.conv2d(self.conv5_1, kernel, [1, 1, 1, 1], padding='SAME')\n",
    "            biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),\n",
    "                                 trainable=True, name='biases')\n",
    "            out = tf.nn.bias_add(conv, biases)\n",
    "            self.conv5_2 = tf.nn.relu(out, name=scope)\n",
    "            self.parameters += [kernel, biases]\n",
    "\n",
    "        # conv5_3\n",
    "        with tf.name_scope('conv5_3') as scope:\n",
    "            kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,\n",
    "                                                     stddev=1e-1), name='weights')\n",
    "            conv = tf.nn.conv2d(self.conv5_2, kernel, [1, 1, 1, 1], padding='SAME')\n",
    "            biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),\n",
    "                                 trainable=True, name='biases')\n",
    "            out = tf.nn.bias_add(conv, biases)\n",
    "            self.conv5_3 = tf.nn.relu(out, name=scope)\n",
    "            self.parameters += [kernel, biases]\n",
    "\n",
    "        # pool5\n",
    "        self.pool5 = tf.nn.max_pool(self.conv5_3,\n",
    "                               ksize=[1, 2, 2, 1],\n",
    "                               strides=[1, 2, 2, 1],\n",
    "                               padding='SAME',\n",
    "                               name='pool4')\n",
    "\n",
    "    def fc_layers(self):\n",
    "        # fc1\n",
    "        with tf.name_scope('fc1') as scope:\n",
    "            shape = int(np.prod(self.pool5.get_shape()[1:]))\n",
    "            fc1w = tf.Variable(tf.truncated_normal([shape, 4096],\n",
    "                                                         dtype=tf.float32,\n",
    "                                                         stddev=1e-1), name='weights')\n",
    "            fc1b = tf.Variable(tf.constant(1.0, shape=[4096], dtype=tf.float32),\n",
    "                                 trainable=True, name='biases')\n",
    "            pool5_flat = tf.reshape(self.pool5, [-1, shape])\n",
    "            fc1l = tf.nn.bias_add(tf.matmul(pool5_flat, fc1w), fc1b)\n",
    "            self.fc1 = tf.nn.relu(fc1l)\n",
    "            self.parameters += [fc1w, fc1b]\n",
    "\n",
    "        # fc2\n",
    "        with tf.name_scope('fc2') as scope:\n",
    "            fc2w = tf.Variable(tf.truncated_normal([4096, 4096],\n",
    "                                                         dtype=tf.float32,\n",
    "                                                         stddev=1e-1), name='weights')\n",
    "            fc2b = tf.Variable(tf.constant(1.0, shape=[4096], dtype=tf.float32),\n",
    "                                 trainable=True, name='biases')\n",
    "            fc2l = tf.nn.bias_add(tf.matmul(self.fc1, fc2w), fc2b)\n",
    "            self.fc2 = tf.nn.relu(fc2l)\n",
    "            self.parameters += [fc2w, fc2b]\n",
    "\n",
    "        # fc3\n",
    "        with tf.name_scope('fc3') as scope:\n",
    "            fc3w = tf.Variable(tf.truncated_normal([4096, 1000],\n",
    "                                                         dtype=tf.float32,\n",
    "                                                         stddev=1e-1), name='weights')\n",
    "            fc3b = tf.Variable(tf.constant(1.0, shape=[1000], dtype=tf.float32),\n",
    "                                 trainable=True, name='biases')\n",
    "            self.fc3l = tf.nn.bias_add(tf.matmul(self.fc2, fc3w), fc3b)\n",
    "            self.parameters += [fc3w, fc3b]\n",
    "\n",
    "    def load_weights(self, weight_file, sess):\n",
    "        weights = np.load(weight_file)\n",
    "        keys = sorted(weights.keys())\n",
    "        for i, k in enumerate(keys):\n",
    "            print(i, k, np.shape(weights[k]))\n",
    "            sess.run(self.parameters[i].assign(weights[k]))\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    sess = tf.Session()\n",
    "    \n",
    "    imgs = tf.placeholder(tf.float32, [None, 224, 224, 3])\n",
    "    \n",
    "    print('Loading model...')\n",
    "    vgg = vgg16(imgs, 'vgg16_weights.npz', sess)\n",
    "    print('Done loading!')\n",
    "    \n",
    "    my_summaries = tf.summary.merge_all()\n",
    "    my_writer = tf.summary.FileWriter('tb_files', sess.graph)\n",
    "\n",
    "    img1 = imread('laska.png', mode='RGB')\n",
    "    img1 = imresize(img1, (224, 224))\n",
    "    \n",
    "    plt.imshow(img1)\n",
    "    plt.title('Input 224x244 image')\n",
    "    plt.show()\n",
    "\n",
    "    prob, fc2_val, my_summaries_protobuf = sess.run([vgg.probs, vgg.fc2, my_summaries], feed_dict={vgg.imgs: [img1]})\n",
    "    prob = prob[0]\n",
    "    my_writer.add_summary(my_summaries_protobuf)\n",
    "    \n",
    "    num_dimensions = np.shape(fc2_val)[1]\n",
    "    plt.bar(range(num_dimensions), fc2_val[0], align='center')\n",
    "    plt.title('{}-dimensional representation of image'.format(num_dimensions))\n",
    "    plt.show()\n",
    "    \n",
    "    print('Top 5 predictions of VGG-16 model:')\n",
    "    preds = (np.argsort(prob)[::-1])[0:5]\n",
    "    for idx, p in enumerate(preds):\n",
    "        print('{}. {} ({})'.format(idx + 1, class_names[p], prob[p]))\n",
    "    sess.close()\n",
    "    "
   ]
  }
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